Category: Yleinen

Authors: Age Poom, Olle Järv, Matthew Zook and Tuuli Toivonen

We have just published our commentary regarding human mobility, mobile Big Data and the responses to COVID-19 pandemic in the Big Data & Society journal (see also the journal’s blog).

This commentary was motivated by our previous experience with mobile Big Data and recent work on changed human mobility derived from mobile phone data during the COVID-19 outbreak in Finland (e.g. Järv et al. 2020a; Järv etal. 2020b). It also draws from the knowledge and experiences of our peers around the globe.

In short, the mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. We can benefit from the avalanche of mobile Big Data that makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. Yet, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, at large, regarding technological, administrative and legislative aspects.

Continue reading “COVID-19 is spatial: Ensuring that mobile Big Data is used for social good”

Overview of Joose Helle’s MSc thesis

Exposure to noise pollution can cause various adverse health effects such as increased blood pressure and stress levels. Noise exposure has traditionally been assessed in terms of home location, as required by national and international policies. However, a substantial share of individuals’ total daily noise exposure is likely to happen while they are on the move. This evidently also affects the healthiness of active travel modes, walking or cycling, by reducing or even outweighing positive health effects of physical activity. Thus, there is a clear need for advancing exposure assessment beyond residential location to gain a true understanding of exposure profiles and their potential effects on health and well-being.

Journey-time exposure assessment can study spatially dynamic exposure to pollutants as people move through the urban environment. Furthermore, least-cost routing can be applied to find healthier paths with lower exposure levels to pollutants. This novel research field has a potential to support urban sustainability and equitability through increasing awareness of the qualities of travel environments, assessing population exposure, supporting individual mobility choices as well as planning healthier travel infrastructure throughout the urban fabric.

These tasks are not trivial. How to assess dynamic exposure to noise pollution? How to find routes with less noise than on the shortest route? How to develop a mobile navigation application for exposure-based route planning? These were the key questions I addressed in my master’s thesis: Quiet paths for people: developing routing analysis and Web GIS application, defended in May 2020 at the University of Helsinki.

Continue reading “Quiet paths for daily travel: developing online navigation and exposure assessment tool”

Authors: Johanna Eklund, Ari-Pekka Jokinen, and Tuuli Toivonen

Introduction

The coronavirus pandemic will have long lasting impacts on the conservation of biodiversity and protected areas. In many Nordic countries, people have been visiting green areas, protected areas included, more than ever. In the Global South however, the situation is almost the opposite, putting conservation at risk. In many developing countries, nature-based tourism has been important for financing biodiversity conservation. Tourism to protected areas has, however, ceased drastically during the global confinement strategies. The consequences of vanishing tourism are very visible for example in Madagascar, one of the world’s biodiversity hotspots, with the highest number of threatened species globally. While being rich in biodiversity, Madagascar is economically among the poorest nations in the world. Madagascar currently has protected 7.5 % of its terrestrial land area, but this protection relies on funding from abroad. Previous research (Eklund et al. 2016 & Eklund et al. 2019) have shown that the protected areas have the capacity to curb deforestation, but funding is needed for the maintenance of conservation actions.

In Madagascar, tourism contribute to approximately 30 % of the annual income of the protected area agency Madagascar National Parks (MNP). MNP is managing some of the most visited national parks in the country. Due to the pandemic there have been concerns raised that the loss of tourism revenue could make the protected areas more vulnerable to illegal encroachment, such as poaching and deforestation. Both Mongabay and National Geographic have reported about these concerns recently. So far the concerns have been based on expert opinions and anecdotal evidence. In this writing, we provide evidence that the protected areas in Madagascar are experiencing rapid increases in human induced fires during the corona crisis.

Continue reading “COVID-19 causes a threat to protected areas in the Global South – evidence from Madagascar”

Parks and other green spaces are an important part of sustainable, healthy and socially equal urban environment. Urban planning and green space management benefit from information about green space use and values, but such data are often scarce and laborious to collect. Temporally dynamic geographic information generated by users of different mobile devices and social media platforms are a promising source of data for studying green spaces.

In a recent article published in the Landscape and Urban Planning journal we compare the ability of different user-generated data sets to provide information on where, when and how people use and value urban green spaces. We compare four types of data: social media, sports tracking, mobile phone operator and public participation geographic information systems (PPGIS) data in a case study from Helsinki, Finland, and ask: 1) where the spatial hot-spots of green space use are, 2) when people use green spaces, 3) what activities are present in green spaces and 4) who are using green spaces based on available sample data sets.

Continue reading “A new paper on understanding the use of urban green spaces from user-generated data”

Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen

According to Statistics Finland (2018), there are more than half a million summer cottages around Finland. They are mostly located in rural areas close to waterbodies – lakes and seaside. In a country with 2.7 million households, such a high number of cottages may change the population patterns drastically once people would move to their cottages in large numbers. This is a potential risk during the epidemic outbreak such as the COVID-19 – health care services are designed mostly based on the permanent population and the cottage dwellers might break the balance in sparsely populated areas. Our preliminary findings already indicated that despite travelling restrictions due to the COVID-19 outbreak in Finland many people escaped from big cities to their summer cottage, see here. But was it really the lure of the summer cottages that explain the mobility patterns to more rural countryside?

Method: We used anonymised and aggregated mobile network data from Telia Crowd Insights such as activity location data as in our previous analyses, and further examined trip data between municipalities. Activity location data indicates the presence of people in a municipality – once a person stays at least 20 minutes in one municipality during one day, then his/her presence is counted as one activity location in given municipality. For example, when having a longer lunch break in a gas station during a long-distance travel. Thus, a person can have several activity locations in different municipalities each day. Trip data is calculated between the two consecutive activity locations at municipality level. In trip data, actual long-distance travel is counted as two different trips, if a longer break is taken during travelling. For example, travel from Helsinki to Rovaniemi by train via Jyväskylä is counted as two trips in data, because train stops at Jyväskylä longer than 20 minutes. These nuanced must be taken into account while reasoning findings.

Continue reading “Towards summer cottages: Mobility flows amid the COVID-19 outbreak in Finland in March”

Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen

Tilastokeskuksen mukaan (2018) Suomessa on yli puoli miljoonaa kesämökkiä. Pääosin ne sijaitsevat harvemmin asutuilla alueilla lähellä vesistöjä. Maassa, jossa on yhteensä 2.7 miljoonaa kotitauloutta, kesämökkien määrä voi vaikuttaa merkittävästi väestön sijoittumiseen, etenkin jos ihmiset siirtyvät kesämökeilleen massoina, kuten tapahtui COVID-19 kriisin alettua. Kun terveyspalvelut ovat pääosin suunniteltu vakituisen väestön pohjalta, kesämökkeilijät voivat merkittävästi lisätä terveyspalveluihin kohdistuvaa taakkaa. Alustavat tuloksemme viittasivat siihen, että moni siirtyi kesämökilleen isoista kaupungeista COVID-19 epidemian seurauksena huolimatta hallituksen matkustusrajoituksista (linkkI). Mutta selittikö kesämökkien houkutus todella löytämämme liikkumisrakenteet?

Menetelmä: Kuten aiemminkin, käytimme Telian Crowd Insights-palvelun anonymisoitua ja yleistettyä mobiiliverkkodataa, kuten aktiviteettisijainteja ja matka-aineistoa. Tilastotasoisista aineistoista ei ole mahdollista tunnistaa yksittäisien ihmisten tai pienten ryhmien liikkeitä. Aktiviteettiaineisto kuvaa väestön oleilua  kunnan alueella, jossa jokainen 20 minuutin oleilu yhden päivän aikana tallennetaan aktiviteetiksi kyseisen kunnan tilastoon. Tällöin esimerkiksi pidempi kahvitauko huoltoasemalla lasketaan aktiivisuussijainniksi ja niitä voi olla useampia päivän aikana eri kuntien alueella. Matka-aineisto puolestaan lasketaan kuntatasolla kahden akvitiviteettisijainnin väliksi. Tällöin matka-aineistossa pitkät matkat voivat tulla lasketuksi kahdeksi tai useammaksi matkaksi riippuen pitkien taukojen määrästä. Esimerkiksi junamatka Helsingistä-Rovaniemellä lasketaan useaksi matkaksi mikäli juna pysähtyy osalla asemista, kuten Tampereella, yli 20 minuutiksi. Nämä tekijät tulee ottaa huomioon tuloksia tulkitessa.

Continue reading “Kesämökkejä kohti – Liikkumisvirrat Suomessa COVID-19 epidemian aikaan”

Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen

Finland, like all other countries in Europe, was hit by the emergence of the covid-19 in March 2020. On Thursday 12th of March, the government gave the first serious recommendations of cancelling big public gatherings and encouraging distant working.  On Monday 16th March the government presented a state of emergency. The schools and public services like museums and libraries were closed and visits to elderly care homes were prohibited. Travel was to be limited to minimum. Historically, the border between Sweden and Finland was closed on the 19th March, restricting the workforce to move across the border in the northern areas and Åland islands. As the glittering snow of Lapland was still attracting tourists from the south, the ski resorts were closed on the 23rd March. As the strongest and an unprecedented measure, the border of the Helsinki-Uusimaa region was closed for personal traffic on the 28th March.  

We at Digital Geography Lab received data from mobile phone operator Telia to analyse how these recommendations and restrictions were followed. Below are some first views on the results, showing how the presence of people changed in the 310 municipalities of Finland during February – March 2020. We concentrate on March and compare the presence of people in different municipalities to the first week of February (1.-7.2.2020) – a baseline week before the start of the school winter holidays and the emergence of Covid 19.  The analysis is based on anonymised and aggregated mobile network data (Telia Crowd Insights activity data). A single activity is recorded of every 20 minute stay in one location. Some February days (12.2., 13.2, 25.2.) are missing in the dataset.

Continue reading “COVID-19 changed the distribution of people in Finland in March – snapshots from aggregated and anonymized mobile network data”

Olle Järv, Elias Willberg, Tuomas, Väisänen, Tuuli Toivonen

Koronavirus mullisti elämämme maaliskuussa 2020. Vapaa liikkuminen loppui ja ihmiset linnoittautuivat koteihinsa mahdollisuuksien mukaan. Torstaina 12.3. hallitus antoi ensimmäiset konkreettiset suositukset liikkumiseen liittyen: etätöitä suositaan ja isot tapahtumat perutaan. Jo seuraavan viikon alussa julistettiin poikkeustila, jonka turvin suljettiin koulut, kirjastot, museot ja vierailut vanhainkoteihin keskeytettiin. Matkailu kehotettiin rajaamaan minimiin yleisesti ja rajantarkistukset otettiin käyttöön kaikilla Suomen rajoilla, myös Ruotsin suuntaan. Tämä rajoitti myös monien työmatkoja Tornionjokilaaksossa ja Ahvenanmaalla.  Pohjoisen hohtavat hanget houkuttelivat vielä väkeä, joten hiihtokeskusten sulkemisesta pikavauhdilla ilmoitettiin 22.3. Merkittävimpänä toimena vapaan liikkumisen Suomessa nähtiin Uudenmaan maakunnan rajan sulkeminen. Tieto sulusta annettiin maaliskuun viimeisellä viikolla ja varsinainen sulku astui voimaan keskiyöllä 28.3. Mitä liikkumiselle oikeastaan tapahtui näiden viikkojen aikana? Jäivätkö suomalaiset kotiin ja missä niin tapahtui ja koska?

Digital Geography Lab tutkimusryhmämme sai käyttöönsä matkapuhelinoperaattori Telian anonymisoitua ja yleistettyä mobiiliverkkodataan perustuvaa kuntatasoista tilastotietoa helmi-maaliskuulta 2020. Data on poiminta Telian Crowd Insights datapalvelusta, ja se kuvaa väkijoukkojen oleskelua tietyssä kunnassa aina yhden päivän aikana vähintään 20 minuuttia kerrallaan. Alla näet joitakin nostoja vertailuistamme eri alueiden ja ajanjaksojen välillä. Vertailuajankohtana käytämme helmikuun ensimmäistä viikkoa, jolloin korona oli vasta kaukomaiden uutinen eivätkä koulujen lomat vielä vaikuttaneet ihmisten liikkumiseen.

Continue reading “Missä ja milloin COVID-19 mullisti liikkumisen? Analyysiä anonymisoidusta ja aggregoidusta mobiiliverkkodatasta”